CNN-based InSAR Coherence Classification

Interferometric Synthetic Aperture Radar (InSAR) imagery based on microwaves reflected off ground targets is becoming increasingly important in remote sensing for ground movement estimation. However, the reflections are contaminated by noise, which distorts the signal's wrapped phase. Demarcati...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2020-01
Main Authors: Mukherjee, Subhayan, Zimmer, Aaron, Sun, Xinyao, Ghuman, Parwant, Cheng, Irene
Format: Article
Language:English
Subjects:
Artificial neural networks
Coherence
Ground motion
Image classification
Interferometric synthetic aperture radar
Microwaves
Qualitative analysis
Radar imaging
Remote sensing
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Interferometric Synthetic Aperture Radar (InSAR) imagery based on microwaves reflected off ground targets is becoming increasingly important in remote sensing for ground movement estimation. However, the reflections are contaminated by noise, which distorts the signal's wrapped phase. Demarcation of image regions based on degree of contamination ("coherence") is an important component of the InSAR processing pipeline. We introduce Convolutional Neural Networks (CNNs) to this problem domain and show their effectiveness in improving coherence-based demarcation and reducing misclassifications in completely incoherent regions through intelligent preprocessing of training data. Quantitative and qualitative comparisons prove superiority of proposed method over three established methods.
ISSN:2331-8422
DOI:10.48550/arxiv.2001.06956